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Python Fastapi Developer Jobs in Canoga Park, CA

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AI Application engineer

Santa Clarita, CA · On-site

... in Python and/or JavaScript/TypeScript . * Experience with backend frameworks (FastAPI, Flask ... Cloud & DevOps * Hands-on experience with cloud platforms (AWS / Azure / GCP). * Experience ...

Commure

Staff Backend Engineer

Los Angeles, CA · On-site

Staff Backend Engineer At Commure, our engineering team is at the forefront of revolutionizing ... Work across the entire technology stack, including but not limited to Python, Go, FastAPI, Flask ...

Flip

Software Engineer

Los Angeles, CA

You will get to work on intricate Python-based systems that interface with a wide range of external ... FastAPI or Pipecat. * Familiarity with REST APIs, authentication methods (OAuth2, JWT, Bearer ...

Commure

Staff Backend Engineer

Los Angeles, CA · On-site

$200K - $250K/yr

Work across the entire technology stack, including but not limited to Python, Go, FastAPI, Flask ... Strong Backend Programming Skills (We use Python and Go) * General Understanding of Containers and ...

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How much do python fastapi developer jobs pay per hour?

As of Jun 3, 2026, the average hourly pay for python fastapi developer in Canoga Park, CA is $61.33, according to ZipRecruiter salary data. Most workers in this role earn between $50.53 and $69.66 per hour, depending on experience, location, and employer.

What is a Python FastAPI Developer job?

A Python FastAPI Developer is responsible for designing, developing, and maintaining backend applications using FastAPI, a modern web framework for building APIs with Python. They work on creating high-performance APIs, integrating with databases, implementing authentication, and ensuring scalability. This role often involves working with asynchronous programming, cloud services, and containerization tools like Docker. Developers collaborate with teams to create efficient, secure, and well-documented API endpoints for web and mobile applications.

What are the key skills and qualifications needed to thrive in the Python Fastapi Developer position, and why are they important?

To thrive as a Python FastAPI Developer, you need strong proficiency in Python programming, experience designing RESTful APIs with FastAPI, and a background in web development concepts. Familiarity with version control systems like Git, containerization tools such as Docker, and knowledge of cloud platforms or SQL/NoSQL databases are commonly required, and certifications in cloud services or Python development can be advantageous. Excellent problem-solving skills, effective communication, and the ability to collaborate in agile teams help developers contribute efficiently to complex projects. These competencies ensure robust, scalable backend solutions and smooth coordination within development teams to meet business goals.

What are some typical daily tasks for a Python FastAPI Developer?

A Python FastAPI Developer typically spends their day designing, developing, and maintaining RESTful APIs to support web or mobile applications. This involves writing clean and efficient Python code, collaborating with frontend developers or other backend engineers to integrate new features, and ensuring the application meets performance and security standards. Developers also participate in code reviews, debugging, and continuous integration processes, while regularly communicating with product managers or stakeholders to align on project requirements. Staying up to date with FastAPI enhancements and industry best practices is also a common part of the role.
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AI Application engineer

AI Application engineer

Inficare

Santa Clarita, CA • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Role - AI Application engineer
Location - Santa Clara, CA (Onsite)
Duration - 12+ months
Need candidate to Prepare , Complete Coding assessment
AI Application engineer who understands data as well as application ; primary databricks and secondary snowflake
Description
Key Responsibilities
  • Design and develop AI-powered applications using machine learning, generative AI, and data-driven services.
  • Integrate ML models, LLMs, and AI services into web, mobile, and enterprise applications.
  • Build production-grade APIs and microservices to serve AI predictions and insights.
  • Collaborate with data scientists and ML engineers to operationalize models.
  • Implement prompt engineering, model orchestration, and inference pipelines.
  • Ensure performance, scalability, security, and reliability of AI applications.
  • Work on real-time and batch AI inference use cases.
  • Implement observability and monitoring for AI behavior and application health.
  • Handle model versioning, rollback strategies, and A/B testing.
  • Ensure compliance with data privacy, responsible AI, and governance standards.
  • Participate in architecture reviews and contribute to AI application best practices.
  • Troubleshoot application and inference issues in production environments.
  • Mentor junior developers and contribute to technical documentation.
Required Skills & Qualifications
Application Development
  • 5-6 years of experience in application development or software engineering.
  • Strong proficiency in Python and/or JavaScript/TypeScript.
  • Experience with backend frameworks (FastAPI, Flask, Django, Node.js).
  • Strong understanding of REST APIs, microservices, and system design.
  • Experience with frontend frameworks is a plus (React, Angular, Vue).
AI & Machine Learning
  • Hands-on experience integrating ML models and AI services into applications.
  • Understanding of ML lifecycle, inference patterns, and model usage.
  • Experience with Generative AI / LLMs (OpenAI, Azure OpenAI, AWS Bedrock, Hugging Face).
  • Knowledge of prompt engineering, context management, and RAG (Retrieval-Augmented Generation).
  • Familiarity with embeddings and vector search.
Data & Backend Integration
  • Strong SQL skills and experience with databases (relational & NoSQL).
  • Experience integrating with data pipelines, feature stores, and analytics systems.
  • Knowledge of APIs, caching layers, and messaging systems (Kafka, RabbitMQ).
Cloud & DevOps
  • Hands-on experience with cloud platforms (AWS / Azure / GCP).
  • Experience deploying AI applications using Docker & Kubernetes.
  • Familiarity with CI/CD pipelines.
  • Experience with cloud-native AI services is a plus.

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